交互式多模型集自适应协同滤波目标跟踪算法研究

为了解决传统交互式多模型算法静态模型集带来的精度低等局限问题,提出了一种多模型集自适应协同滤波算法.通过比较目标与当前模型集中不同模型之间的模型匹配概率,自动确定当前模型匹配中的最好模型与最坏模型,利用激活、保留和剔除策略改变固定模型集的结构以达到模型集自适应的过程.通过与其他已经提出的交互式多模型算法进行比较,实验结...

Research on Interactive Multi-model Set Adaptive Collaborative Filtering Target Tracking Algorithm

In order to solve the limitation of the static model set of the traditional interactive multiple model(IMM)algorithm, a interactive multi-model set adaptation collaborative filtering(SAC-IMM)algorithm is proposed. By calculating the model matching probability between the target and different models in the current model set, the best model and the worst model in the current model match are automatically determined. The adaptive process of the model set is achieved by changing the structure of the static model set by using activation, retention and elimination strategies. Compared with the traditional IMM algorithm, the SAC-IMM algorithm proposed has a certain degree of improvement in positioning accuracy. By comparing with other IMM algorithms that have been proposed, the experimented results show that the SAC-IMM algorithm proposed has been optimized for state estimation of speed, acceleration, and turning rate. The proposed method can improve the accuracy of target tracking and positioning to a certain extent.